
Nonlinear Filters
Estimation and Applications
Hisashi Tanizaki(Author)
Springer (Publisher)
2nd Edition
Published on 1. December 2010
Book
Paperback/Softback
XIX, 256 pages
978-3-642-08253-5 (ISBN)
Description
Nonlinear and nonnormal filters are introduced and developed. Traditional nonlinear filters such as the extended Kalman filter and the Gaussian sum filter give biased filtering estimates, and therefore several nonlinear and nonnormal filters have been derived from the underlying probability density functions. The density-based nonlinear filters introduced in this book utilize numerical integration, Monte-Carlo integration with importance sampling or rejection sampling and the obtained filtering estimates are asymptotically unbiased and efficient. By Monte-Carlo simulation studies, all the nonlinear filters are compared. Finally, as an empirical application, consumption functions based on the rational expectation model are estimated for the nonlinear filters, where US, UK and Japan economies are compared.
More details
Edition
Second Edition 1996
Language
English
Place of publication
Berlin
Germany
Publishing group
Springer Berlin
Target group
Professional and scholarly
Research
Illustrations
1 s/w Abbildung
XIX, 256 p. 1 illus.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 16 mm
Weight
429 gr
ISBN-13
978-3-642-08253-5 (9783642082535)
DOI
10.1007/978-3-662-03223-7
Schweitzer Classification
Other editions
Additional editions

Book
08/1996
2nd Edition
Springer
€106.99
Shipment within 10-15 days
Content
1. Introduction.- 2. State-Space Model in Linear Case.- 3. Traditional Nonlinear Filters.- 4. Density-Based Nonlinear Filters.- 5. Monte-Carlo Experiments.- 6. Application of Nonlinear Filters.- 7. Prediction and Smoothing.- 8. Summary and Concluding Remarks.- References.